{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "027ff9dc-dd98-4846-8edd-5182a7acd2ac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import joblib\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings; warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c0c3a27-2f0a-4630-9d3e-6f008a277ba3",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [-0.43425145,  0.00380549,  0.43805694, ...,  1.20552059,\n",
       "          1.66197859, -0.76743238],\n",
       "        [-0.40663559,  0.01588737,  0.42252296, ...,  1.20201614,\n",
       "          1.65950262, -0.76712383],\n",
       "        [-0.38198727,  0.02548188,  0.40746919, ...,  1.20299696,\n",
       "          1.65952942, -0.75982732]],\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ]]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with h5py.File('./data/ecg_tracings.hdf5', \"r\") as f:\n",
    "    X = np.array(f['tracings'])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c91e3c23-4e35-4175-8543-1469adf37a1c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(827, 4096, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f46a9e3d-ecc0-4ee8-9d34-a95f98ae6cee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9b694d4250>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4x downsample, then crop out 12 points on axis 1, both sides\n",
    "\n",
    "X_down = X[:, ::4, :]\n",
    "X_down = X_down[:, 12:-12, :]\n",
    "assert X_down.shape == (827, 1000, 12)\n",
    "plt.plot(X_down[0,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb3b119-44a7-4ea4-8e32-3cd71e1e8794",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c20d2d9-7556-41b3-9ac1-b8b14e716ce7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9b1802a370>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Do later if results are not good\n",
    "scaler = MinMaxScaler((-1, 1))\n",
    "\n",
    "x = X_down[0,:,:]\n",
    "x_ = scaler.fit_transform(x)\n",
    "\n",
    "plt.plot(X_down[0,:,0]) # original\n",
    "plt.plot(x_[:, 0]) # scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e4cd0ee-3338-46f3-b204-497c91132e69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 827/827 [00:00<00:00, 2039.93it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9b6972de50>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler = MinMaxScaler((-1, 1))\n",
    "\n",
    "for i in tqdm(range(X_down.shape[0])):\n",
    "    X_down[i, :, :] = scaler.fit_transform(X_down[i, :, :])\n",
    "\n",
    "plt.plot(X_down[0,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42fea0ee-1ad2-406d-8923-d903064a860f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "df1ee522-3d08-488e-8cbf-7c7c1dddf63a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(827, 6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.read_csv('./data/annotations/gold_standard.csv')\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1b16ea7-8bd5-4f72-a6dd-4011fb7d5fda",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y['NORM'] = y.apply(\n",
    "    lambda row: 1 if row[['1dAVb', 'RBBB', 'LBBB', 'SB', 'AF', 'ST']].sum() == 0 else 0,\n",
    "    axis = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a908a61-73ce-4ac8-8453-5aa5d7b9097b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NORM</th>\n",
       "      <th>1dAVb</th>\n",
       "      <th>RBBB</th>\n",
       "      <th>LBBB</th>\n",
       "      <th>SB</th>\n",
       "      <th>AF</th>\n",
       "      <th>ST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>827 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     NORM  1dAVb  RBBB  LBBB  SB  AF  ST\n",
       "0       1      0     0     0   0   0   0\n",
       "1       0      0     0     1   0   0   0\n",
       "2       1      0     0     0   0   0   0\n",
       "3       1      0     0     0   0   0   0\n",
       "4       1      0     0     0   0   0   0\n",
       "..    ...    ...   ...   ...  ..  ..  ..\n",
       "822     1      0     0     0   0   0   0\n",
       "823     1      0     0     0   0   0   0\n",
       "824     1      0     0     0   0   0   0\n",
       "825     1      0     0     0   0   0   0\n",
       "826     0      1     0     0   0   0   0\n",
       "\n",
       "[827 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y[['NORM', '1dAVb', 'RBBB', 'LBBB', 'SB', 'AF', 'ST']] # rearrange column\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90661c6c-3e09-4ce6-ba4a-b7aef28c4354",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82345828, 0.03385732, 0.04111245, 0.0362757 , 0.01934704,\n",
       "       0.01571947, 0.04474002])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y.sum(axis=0)/len(y)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ea5d653-7aa4-4b68-95bc-4e198c92f9e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61b167cc-6d16-4afb-9584-369b56005a91",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CLASSES = ['NORM', '1dAVb', 'RBBB', 'LBBB', 'SB', 'AF', 'ST']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d3759b3-fda3-4ab9-9eee-67fc178b3beb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_down, y, test_size=0.33, random_state=1)\n",
    "\n",
    "assert X_train.shape[1] == 1000\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8c95ec0-0fe1-48de-b5aa-559d057eb4ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data/y_test.joblib']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(X_train, './data/X_train.joblib')\n",
    "joblib.dump(y_train, './data/y_train.joblib')\n",
    "joblib.dump(X_test, './data/X_test.joblib')\n",
    "joblib.dump(y_test, './data/y_test.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c46f8e1-6501-4854-8f00-6f4c1d6ba6a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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